Towards a Neuroscience of Human Emotion

Abstract
Emotions are powerful organizers of perceptual, mnemonic, motivational, and physiological processes. Understanding emotions, and their basic affective and cognitive ingredients, is essential for understanding healthy and disordered brain function. However, in spite of some claims to the contrary in the popular press, there are as yet no reliable human brain markers for affective processes. Here, I introduce an analysis framework for identifying fMRI patterns specific to particular types of mental events. This framework is qualitatively different from the "brain mapping" approach because it emphasizes a) optimization of psychological ("reverse") inference using machine learning; b) quantitative assessment of the diagnostic value of brain patterns; and c) prospective use of the same diagnostic patterns across studies. Using this approach, we have identified a brain pattern that can predict the intensity of physical pain at the level of the individual person with 90-100% accuracy in some tests. This brain marker for pain is distinct from other patterns that are diagnostic of other types of affective events (e.g., observed pain, aversive images, and romantic rejection). These results suggest that it may be possible to develop fMRI-based brain markers for distinct emotional states. Such markers would provide new ways of measuring and classifying emotions, characterizing brain disorders, and testing the effects of cognitive manipulations on pain and emotion.